In literature, common techniques are generally based on the aggregation of frame-level deep features computed on a pretrained convolutional neural network (CNN) model [1]}, [2]}, [3]}, [4]}, [5]}. However, even if the techniques modeling only the spatial interdependencies of deep features attain superior performances for large-scale data, they are not able to capture the temporal structure between these features [1]}, [5]}. Similarly, pooling techniques based on temporal models [3]}, [9]} are inclined to overfit to the training data and irrelevant results can be obtained for unseen examples. Recently, [10]} shows that random frame sampling yields similar results for the temporal models compared to dense frame sequences. This is a critical observation for which temporal models have some difficulties to learn/generalize complex spatio-temporal structures between actions from coarse frame-level features.
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